Improved Fourier Transform Method for Unsupervised Cell-Cycle Regulated Gene Prediction

Motivation: Cell-cycle regulated gene prediction using microarray time-course measurements of the mRNA expression levels of genes has been used by several researchers. The popularly employed approach is Fourier transform (FT) method in conjunction with the set of known cell-cycle regulated genes. In the absence of training data, fourier transform method is sensitive to noise, additive monotonic component arising from cell population growth and deviation from strict sinusoidal form of expression. Known cell cycle regulated genes may not be available for certain organisms or using them for training may bias the prediction. Results: In this paper we propose an Improved Fourier Transform (IFT) method which takes care of several factors such as monotonic additive component of the cell-cycle expression, irregular or partial-cycle sampling of gene expression. The proposed algorithm does not need any known cell-cycle regulated genes for prediction. Apart from alleviating need for training set...

• Type: Conference
• Year: 2004
• Where: CSB
• Authors: Karuturi R. Krishna Murthy, Liu Jian Hua
• Keywords: Bioinformatics, Cell-cycle Regulated Genes, CSB 2004, Fourier Transform, Fourier Transform Method